DOA tracking for seamless connectivity in beamformed IoT-based drones

In recent times, there has been a surge of interest around the usage of adaptive antenna arrays of Internet of Things (IoT) based Drones in the communication systems. Adaptive antenna arrays have the ability to form customized radiation patterns based on the changes in the environment by employing methods for estimating Direction of Arrival (DOA) and adaptive beamforming. Nevertheless, upon deploying adaptive antenna arrays in complex IoT platforms, the radiation patterns that result from the use of such adaptive algorithms may be adjusted to the preceding location of the node and not attuned to the current location. These issues that arise due to mobility can be resolved by continuously tracking the DOA of the intended target. As DOA is time varying in an IoT Drone environment, existing algorithms for estimating the DOA like MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) cannot be used to track the signal subspace recursively, as they are based on batch eigenvalue decomposition which is highly time consuming with a time complexity of O(n3). Furthermore, DOA estimation algorithms do not result in robust subspace estimates when the Signal to Noise Ratio (SNR) is low.The main novelty of the proposed work is a low computational complexity subspace tracking algorithm for tracking DOA in order to provide seamless connectivity. Simulation results show that the proposed DOA tracking takes lesser time for tracking the current location of the drone target as opposed to conventional DOA estimation methods. Furthermore,it is observed that the tracking process remains unaffected by SNR.